• Journal of Computational Design and Engineering
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• 제1권1호
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• pp.48-54
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• 2014

In this paper, the authors propose a system for assisting mold designers of plastic parts. With a CAD model of a part, the system automatically determines the optimal ejecting direction of the part with minimum undercuts. Since plastic parts are generally very thin, many rib features are placed on the inner side of the part to give sufficient structural strength. Our system extracts the rib features from the CAD model of the part, and determines the possible ejecting directions based on the geometric properties of the features. The system then selects the optimal direction with minimum undercuts. Possible ejecting directions are represented as discrete points on a Gauss map. Our new point distribution method for the Gauss map is based on the concept of the architectural geodesic dome. A hierarchical structure is also introduced in the point distribution, with a higher level "rough" Gauss map with rather sparse point distribution and another lower level "fine" Gauss map with much denser point distribution. A system is implemented and computational experiments are performed. Our system requires less than 10 seconds to determine the optimal ejecting direction of a CAD model with more than 1 million polygons.

• 한국정밀공학회지
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• 제19권6호
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• pp.29-34
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• 2002

This paper presents formation of ejector-pin hollowness in injection molding. Injection molding process is widely used in production of plastic part for good dimensional accuracy and high productivity. However, the injection molding leaves ejector-pin hollowness on pal, which causes bad part surface and quality. Dimensions and profiles of ejector-pin hollowness are measured for formation or ejector-pin hollowness. The formation of ejector-pin hollowness is traced with dimensions and profiles of ejector-pin hollowness. The compression force and moment cause ejector-pin to form hollowness on part surface.

• International Journal of Precision Engineering and Manufacturing
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• 제4권2호
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• pp.49-56
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• 2003

As semi-solid forging (SSF) is compared with conventional casting such as gravity die-casting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally, SSF consists of reheating, forging, and ejecting processes. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power has large effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time for predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted from the reheating experiments. Results by neural network were in good agreement with those by experiment. Polynominal regression analysis was formulated using the test data from neural network. Optimum processing condition was calculated to minimize the grain size and solid fraction standard deviation or to maximize the specimen temperature average. Discussion is given about reheating process of row material and results are presented with regard to accurate process variables fur proper solid fraction, specimen temperature and grain size.

• 한국정밀공학회지
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• 제19권2호
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• pp.114-125
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• 2002

As semi-solid forging (SSF) is compared with conventional easting such as gravity die-easting and squeeze casting, the product without inner defects can be obtained from semi-solid forming and globular microstructure as well. Generally speaking. SSF consists of reheating, forging, ejecting precesses. In the reheating process, the materials are heated up to the temperature between the solidus and liquidus line at which the materials exists in the form of liquid-solid mixture. The process variables such as reheating time, reheating temperature, reheating holding time, and induction heating power have much effect on the quality of the reheated billets. It is difficult to consider all the variables at the same time when predicting the quality. In this paper, Taguchi method, regression analysis and neural network were applied to analyze the relationship between processing conditions and solid fraction. A356 alloy was used for the present study, and the learning data were extracted by the reheating experiments. Results by neural network were on good agreement with those by experiment. Polynominal regression analysis was formulated by using the test data from neural network. Optimum processing condition was calculated to minimize the grain size, solid fraction standard deviation, otherwise, to maximize the specimen temperature average. In this time, discussion is liven about reheating process of row material and results are presented with regard to accurate process variables for proper solid fraction, specimen temperature and grain size.